This data notebook is based on a model presented at the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.
If you want to cite the method/model please use:
Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at the International Conference on Evolving Cities, MAST Mayflower Studios, Southampton, United Kingdom. 22 - 24 Sep 2021.
If you are interested in how the model works start from https://dataknut.github.io/localCarbonTaxModels/
If you wish to re-use material from this data notebook please cite it as:
Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale: Wider Solent/‘Pan-Hampshire’ as a case study, University of Southampton, United Kingdom
License: CC-BY
Share, adapt, give attribution.
This data notebook estimates the value of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. These emissions are all consumption, gas and electricity. It does this under two scenarios - a simple carbon value multiplier and a rising block tariff.
It then compares these with estimates of the cost of retrofitting EPC band dwellings D-E and F-G in each LSOA and for the whole area under study.
Key results:
Background blurb about emissions, retofit, carbon tax/levy etc
In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. We then sum these values to given an overall levy revenue estimate for the area in the case study.
We then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.
We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA or in the case styudy area to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required. It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA hetergeneity in emissions and so will almost certaonly underestimate the range of the household level emissions levy value.
We will use a number of datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).
This analysis is at LSOA level.
Load LSOA look-up table and list the local authoroties we are covering.
## Loading LSOA look-up table with useful labels
## [1] "LSOA11CD" "LSOA11NM" "MSOA11NM" "LA11NM"
## [5] "WD20CD" "WD20NM" "RUC11" "Supergroup Name"
## [9] "LAD11NM"
## Number of LSOAs covered
## [1] 1194
## Number of local authorities covered
## [1] 14
## Using 'n_LSOAs' as value column. Use 'value.var' to override
LA11NM | Rural town and fringe | Rural village and dispersed | Urban city and town |
Basingstoke and Deane | 15 | 14 | 80 |
East Hampshire | 13 | 11 | 48 |
Eastleigh | 6 | 71 | |
Fareham | 73 | ||
Gosport | 53 | ||
Hart | 10 | 8 | 39 |
Havant | 2 | 76 | |
Isle of Wight | 19 | 9 | 61 |
New Forest | 18 | 13 | 83 |
Portsmouth | 125 | ||
Rushmoor | 58 | ||
Southampton | 148 | ||
Test Valley | 5 | 18 | 48 |
Winchester | 21 | 18 | 31 |
LSOA - this is all going to be LSOA analysis
## Loading Solent LSOA boundaries from file
## Rows of data: 1136
Check with a map…
## Boundary data co-ord system: 27700
Figure 5.1: LSOA check map (shows LSOA, MSOA and ward names when clicked
Labeled as 2019 but actually 2018 data. Source: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019
## Overall IMD decile counts
## [1] 32844
##
## 1 (10% most deprived) 2 3
## 3284 3284 3285
## 4 5 6
## 3284 3285 3284
## 7 8 9
## 3284 3285 3284
## 10 (10% least deprived)
## 3285
## # Solent IMD decile counts
## [1] 1194
##
## 1 (10% most deprived) 2 3
## 44 81 94
## 4 5 6
## 122 115 103
## 7 8 9
## 122 134 159
## 10 (10% least deprived)
## 220
##
## 1 (10% most deprived) 2 3
## 0.03685092 0.06783920 0.07872697
## 4 5 6
## 0.10217755 0.09631491 0.08626466
## 7 8 9
## 0.10217755 0.11222781 0.13316583
## 10 (10% least deprived)
## 0.18425461
##
## 50% least deprived 50% most deprived
## 738 456
##
## 50% least deprived 50% most deprived
## 0.6180905 0.3819095
These are LSOA level deprivation indices. Decile is the English & Welsh decile:
Figure 5.2: LSOA IMD map (shows LSOA, MSOA, ward names and IMD decile when clicked
2019 estimates - do we actually use this data?
Source: https://www.gov.uk/government/statistics/sub-regional-fuel-poverty-data-2021
See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/
“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”
“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."
Source: https://www.carbon.place/
Notes:
## [1] 32844
| Name | credsLsoaDT |
| Number of rows | 1194 |
| Number of columns | 30 |
| Key | LSOA11CD |
| _______________________ | |
| Column type frequency: | |
| character | 7 |
| factor | 1 |
| numeric | 22 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| LAD11NM | 0 | 1 | 4 | 21 | 0 | 14 | 0 |
| WD18NM | 0 | 1 | 4 | 42 | 0 | 292 | 0 |
| LSOA11CD | 0 | 1 | 9 | 9 | 0 | 1194 | 0 |
| LSOA11NM | 0 | 1 | 9 | 26 | 0 | 1194 | 0 |
| WD20CD | 0 | 1 | 9 | 9 | 0 | 282 | 0 |
| RUC11 | 0 | 1 | 19 | 27 | 0 | 3 | 0 |
| oacSuperGroupName | 0 | 1 | 15 | 35 | 0 | 8 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| IMD_Decile_label | 0 | 1 | FALSE | 10 | 10 : 220, 9: 159, 8: 134, 4: 122 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| CREDStotal_kgco2e | 0 | 1.00 | 15183049.41 | 5154042.47 | 4818400.00 | 11606400.00 | 14731500.00 | 18590000.00 | 37236000.00 | ▅▇▅▁▁ |
| CREDSgas_kgco2e2018 | 0 | 1.00 | 1543093.13 | 601806.98 | 0.00 | 1199077.50 | 1495220.00 | 1849125.00 | 5108580.00 | ▂▇▂▁▁ |
| CREDSelec_kgco2e2018 | 0 | 1.00 | 782210.06 | 266915.81 | 350200.00 | 613800.00 | 714560.00 | 872990.00 | 2478420.00 | ▇▃▁▁▁ |
| CREDSotherEnergy_kgco2e2011 | 0 | 1.00 | 136491.84 | 318307.76 | 0.00 | 22221.50 | 40244.50 | 76161.25 | 2315490.00 | ▇▁▁▁▁ |
| CREDSmeteredHomeEnergy_kgco2e2018 | 0 | 1.00 | 2325303.19 | 701431.75 | 622710.00 | 1865605.00 | 2212690.00 | 2657880.00 | 7587000.00 | ▅▇▁▁▁ |
| CREDSallHomeEnergy_kgco2e2018 | 0 | 1.00 | 2461795.03 | 754979.54 | 1048950.00 | 1963222.50 | 2321827.00 | 2811340.00 | 8553640.00 | ▇▅▁▁▁ |
| CREDScar_kgco2e2018 | 0 | 1.00 | 1861521.96 | 714842.21 | 333540.00 | 1357290.00 | 1768650.00 | 2237750.00 | 5014000.00 | ▃▇▃▁▁ |
| CREDSvan_kgco2e2018 | 0 | 1.00 | 305585.01 | 898342.37 | 11328.00 | 126901.25 | 183270.00 | 278405.00 | 18232800.00 | ▇▁▁▁▁ |
| pop_2018 | 0 | 1.00 | 1663.18 | 391.28 | 947.00 | 1430.00 | 1590.00 | 1780.00 | 5620.00 | ▇▂▁▁▁ |
| energy_pc | 28 | 0.98 | 17.39 | 5.40 | 6.67 | 13.52 | 16.40 | 20.47 | 44.48 | ▅▇▂▁▁ |
| pc_Heating_Electric | 0 | 1.00 | 9.53 | 9.42 | 0.13 | 3.48 | 6.37 | 12.52 | 85.27 | ▇▁▁▁▁ |
| epc_total | 0 | 1.00 | 422.65 | 180.14 | 153.00 | 309.00 | 376.00 | 492.00 | 2350.00 | ▇▁▁▁▁ |
| epc_newbuild | 0 | 1.00 | 80.15 | 127.29 | 1.00 | 27.00 | 46.00 | 80.00 | 1960.00 | ▇▁▁▁▁ |
| epc_A | 0 | 1.00 | 0.78 | 3.15 | 0.00 | 0.00 | 0.00 | 0.00 | 48.00 | ▇▁▁▁▁ |
| epc_B | 0 | 1.00 | 56.91 | 114.81 | 0.00 | 7.00 | 21.00 | 60.00 | 1800.00 | ▇▁▁▁▁ |
| epc_C | 0 | 1.00 | 128.94 | 78.50 | 18.00 | 74.00 | 108.00 | 161.00 | 688.00 | ▇▃▁▁▁ |
| epc_D | 0 | 1.00 | 163.42 | 47.81 | 4.00 | 134.00 | 160.00 | 185.00 | 413.00 | ▁▇▅▁▁ |
| epc_E | 0 | 1.00 | 55.72 | 32.21 | 0.00 | 34.00 | 50.00 | 72.00 | 243.00 | ▇▇▁▁▁ |
| epc_F | 0 | 1.00 | 13.26 | 16.59 | 0.00 | 4.00 | 8.00 | 16.00 | 196.00 | ▇▁▁▁▁ |
| epc_G | 0 | 1.00 | 3.63 | 6.04 | 0.00 | 0.00 | 2.00 | 4.00 | 96.00 | ▇▁▁▁▁ |
| IMD_Decile | 0 | 1.00 | 6.47 | 2.78 | 1.00 | 4.00 | 7.00 | 9.00 | 10.00 | ▂▅▅▆▇ |
| IMDScore | 0 | 1.00 | 16.77 | 12.38 | 0.95 | 7.14 | 13.39 | 22.90 | 72.08 | ▇▅▂▁▁ |
##
## Basingstoke and Deane East Hampshire Eastleigh
## 109 72 77
## Fareham Gosport Hart
## 73 53 57
## Havant Isle of Wight New Forest
## 78 89 114
## Portsmouth Rushmoor Southampton
## 125 58 148
## Test Valley Winchester
## 71 70
## [1] 1194
Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings
Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)
First check the n electricity meters logic…
## LSOAs (check):
## [1] 1194
## [1] 1194
Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.
That assumption seems sensible…
We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.
## # Summary of per dwelling values
| Name | …[] |
| Number of rows | 1194 |
| Number of columns | 9 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| CREDStotal_kgco2e_pdw | 0 | 1 | 21834.16 | 8865.44 | 5802.13 | 15089.90 | 20566.08 | 27272.40 | 52773.03 | ▅▇▅▂▁ |
| CREDSgas_kgco2e2018_pdw | 0 | 1 | 2150.21 | 726.47 | 0.00 | 1784.09 | 2175.64 | 2624.55 | 4333.54 | ▁▂▇▃▁ |
| CREDSelec_kgco2e2018_pdw | 0 | 1 | 1072.63 | 243.32 | 582.86 | 933.02 | 1012.61 | 1115.83 | 2272.31 | ▂▇▁▁▁ |
| CREDSmeteredHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3222.84 | 728.77 | 1123.58 | 2738.86 | 3166.69 | 3689.29 | 6404.30 | ▁▇▆▁▁ |
| CREDSotherEnergy_kgco2e2011_pdw | 0 | 1 | 180.66 | 404.84 | 0.00 | 32.77 | 54.67 | 103.88 | 2984.01 | ▇▁▁▁▁ |
| CREDSallHomeEnergy_kgco2e2018_pdw | 0 | 1 | 3403.50 | 737.23 | 1506.11 | 2873.42 | 3305.60 | 3858.38 | 6790.95 | ▂▇▅▁▁ |
| CREDScar_kgco2e2018_pdw | 0 | 1 | 2621.81 | 959.27 | 457.84 | 1909.43 | 2523.53 | 3295.55 | 5174.83 | ▂▇▇▅▂ |
| CREDSvan_kgco2e2018_pdw | 0 | 1 | 409.99 | 1109.82 | 22.61 | 180.18 | 255.48 | 394.30 | 32186.23 | ▇▁▁▁▁ |
| CREDSpersonalTransport_kgco2e2018_pdw | 0 | 1 | 3031.80 | 1488.24 | 528.81 | 2144.14 | 2885.36 | 3714.77 | 33215.59 | ▇▁▁▁▁ |
Examine patterns of per dwelling emissions for sense.
Figure 5.3 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.
## Per dwelling T CO2e - all emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.3: Scatter of LSOA level all per dwelling emissions against IMD score
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$IMDScore and credsLsoaDT$CREDStotal_kgco2e_pdw
## t = -28.11, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6643148 -0.5959946
## sample estimates:
## cor
## -0.6313783
## LAD11NM WD18NM LSOA11CD All_Tco2e_per_dw
## Length:1194 Length:1194 Length:1194 Min. : 5.802
## Class :character Class :character Class :character 1st Qu.:15.090
## Mode :character Mode :character Mode :character Median :20.566
## Mean :21.834
## 3rd Qu.:27.272
## Max. :52.773
LAD11NM | WD18NM | LSOA11CD | All_Tco2e_per_dw |
Isle of Wight | Newport East | E01017332 | 5.8 |
Southampton | Bargate | E01017140 | 5.8 |
Portsmouth | Charles Dickens | E01033381 | 6.1 |
Portsmouth | St Thomas | E01017133 | 6.4 |
Southampton | Bargate | E01017139 | 7.0 |
Isle of Wight | Newport Central | E01017326 | 7.0 |
LAD11NM | WD18NM | LSOA11CD | All_Tco2e_per_dw |
Hart | Hartley Wintney | E01022858 | 52.8 |
Hart | Odiham | E01032855 | 51.6 |
Basingstoke and Deane | Hatch Warren and Beggarwood | E01022511 | 51.1 |
Hart | Fleet East | E01032857 | 50.7 |
Basingstoke and Deane | Hatch Warren and Beggarwood | E01022512 | 48.9 |
Fareham | Fareham North | E01022729 | 48.5 |
Are there any strange outliers?
Figure 5.4 maps retrofit costs
Figure 5.4: Total emissios
Figure 5.5 uses the same plotting method to show emissions per dwelling due to gas use. This preserves the negative correlation shown in the previous plot for ‘all emissions’ but with some variation, notably for LSOAs which have a higher % ofelectric heating.
## Per dwelling T CO2e - gas emissions
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 1784 2176 2150 2625 4334
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.5: Scatter of LSOA level gas per dwelling emissions against IMD score
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$IMDScore and credsLsoaDT$CREDSgas_kgco2e2018_pdw
## t = -21.557, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5692532 -0.4875429
## sample estimates:
## cor
## -0.5296256
Are there any strange outliers? Could be an issue with the gas meter data to LSOA allocation…
Figure 5.6 uses the same plotting method to show emissions per dwelling due to electricity use. This is usuallu much more random… although note the LSOAs with higher % electric heating.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.6: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$IMDScore and credsLsoaDT$CREDSelec_kgco2e2018_pdw
## t = -9.5232, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3178384 -0.2123733
## sample estimates:
## cor
## -0.2659013
Figure 5.7 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.7: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?
## Correlation test (pwcorr)
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$IMDScore and credsLsoaDT$CREDSelec_kgco2e2018_pdw
## t = -9.5232, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3178384 -0.2123733
## sample estimates:
## cor
## -0.2659013
RUC11 | mean_gas_kgco2e | mean_elec_kgco2e | mean_other_energy_kgco2e |
Rural town and fringe | 2,414.5 | 1,120.4 | 229.9 |
Rural village and dispersed | 1,415.9 | 1,694.1 | 1,249.0 |
Urban city and town | 2,190.5 | 1,009.3 | 75.4 |
Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$CREDStotal_kgco2e_pdw and credsLsoaDT$CREDSmeteredHomeEnergy_kgco2e2018_pdw
## t = 26.567, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5729367 0.6442871
## sample estimates:
## cor
## 0.6098462
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Strong correlation. So in theory we could (currently) use measured energy emissions as a proxy for total emissions.
Repeat for all home energy - includes estimates of emissions from oil etc
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$CREDStotal_kgco2e_pdw and credsLsoaDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 34.154, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6734136 0.7308495
## sample estimates:
## cor
## 0.7032774
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Slightly weaker correlation…
We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)
Figure 5.8 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.
## Per dwelling T CO2e - car emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.8: Scatter of LSOA level car use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$IMDScore and credsLsoaDT$CREDScar_kgco2e2018_pdw
## t = -30.024, df = 1192, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6873457 -0.6226502
## sample estimates:
## cor
## -0.6562022
## RUC11 mean_car_kgco2e mean_van_kgco2e
## 1: Rural town and fringe 3085.434 370.1960
## 2: Rural village and dispersed 3977.615 745.3948
## 3: Urban city and town 2445.047 382.8929
Figure 5.9 uses the same plotting method to show emissions per dwelling due to van use.
## Per dwelling T CO2e - van emissions
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.9: Scatter of LSOA level van use per dwelling emissions against IMD score
## Correlation with IMD score (pwcorr)
##
## Pearson's product-moment correlation
##
## data: credsLsoaDT$IMDScore and credsLsoaDT$CREDSvan_kgco2e2018_pdw
## t = 0.99035, df = 1192, p-value = 0.3222
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02810456 0.08526582
## sample estimates:
## cor
## 0.02867284
LAD11NM | WD18NM | LSOA11CD | CREDSvan_kgco2e2018_pdw |
Southampton | Bargate | E01017137 | 22.6 |
Southampton | Bargate | E01017138 | 34.1 |
Portsmouth | Charles Dickens | E01033381 | 43.3 |
Southampton | Bargate | E01032755 | 43.9 |
Portsmouth | St Thomas | E01017130 | 45.5 |
Basingstoke and Deane | Eastrop | E01032842 | 50.5 |
LAD11NM | WD18NM | LSOA11CD | CREDSvan_kgco2e2018_pdw |
Portsmouth | Cosham | E01017053 | 32,186.2 |
Rushmoor | Wellington | E01023142 | 13,358.5 |
Test Valley | Romsey Extra | E01023199 | 11,077.0 |
Winchester | Wonston and Micheldever | E01023288 | 4,671.2 |
Eastleigh | Chandler's Ford | E01022665 | 3,603.1 |
Test Valley | Harroway | E01023180 | 3,516.3 |
Are there any strange outliers? Could be an issue with the CREDS model…
In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…
Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.
## N EPCs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 153.0 309.0 376.0 422.6 492.0 2350.0
## N elec meters
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 325.0 619.2 691.0 731.8 802.0 2638.0
Correlation between high % EPC F/G or A/B and deprivation?
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Now we need to convert the % to dwellings using the number of electricity meters (see above).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Case studies:
BEIS/ETC Carbon ‘price’
EU carbon ‘price’
Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)
The table below shows the overall £ GBP total for the case study area in £M.
## beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1: 4441.5 451.4 228.82
The table below shows the mean per dwelling value rounded to the nearest £10.
## beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw
## 1: 5350 530 260
## beis_GBPtotal_c_energy_perdw
## 1: 790
Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.10: £k per LSOA revenue using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.11: £k per LSOA revenue using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1422 3697 5039 5349 6682 12929
LSOA11CD | WD18NM | CREDStotal_kgco2e_pdw | beis_GBPtotal_c_perdw |
E01022858 | Hartley Wintney | 52,773.0 | 12,929.4 |
E01032855 | Odiham | 51,606.1 | 12,643.5 |
E01022511 | Hatch Warren and Beggarwood | 51,113.3 | 12,522.8 |
E01032857 | Fleet East | 50,693.6 | 12,419.9 |
E01022512 | Hatch Warren and Beggarwood | 48,911.4 | 11,983.3 |
E01022729 | Fareham North | 48,537.6 | 11,891.7 |
LSOA11CD | WD18NM | CREDStotal_kgco2e_pdw | beis_GBPtotal_c_perdw |
E01017332 | Newport East | 5,802.1 | 1,421.5 |
E01017140 | Bargate | 5,845.6 | 1,432.2 |
E01033381 | Charles Dickens | 6,065.4 | 1,486.0 |
E01017133 | St Thomas | 6,367.7 | 1,560.1 |
E01017139 | Bargate | 7,015.4 | 1,718.8 |
E01017326 | Newport Central | 7,017.0 | 1,719.2 |
Figure ?? repeats the analysis but just for gas.
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.12: £k per LSOA incurred via gas using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.13: £k per LSOA incurred via gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 437.1 533.0 526.8 643.0 1061.7
LSOA11CD | WD18NM | gasTCO2e_pdw | beis_GBPtotal_c_gas_perdw |
E01023171 | Chilworth, Nursling and Rownhams | 4.3 | 1,061.7 |
E01022706 | Hiltingbury | 4.3 | 1,055.7 |
E01022602 | Headley | 4.2 | 1,031.8 |
E01022998 | Brockenhurst and Forest South East | 4.0 | 979.8 |
E01022629 | Rowlands Castle | 3.9 | 962.8 |
E01022855 | Odiham | 3.9 | 960.1 |
LSOA11CD | WD18NM | gasTCO2e_pdw | beis_GBPtotal_c_gas_perdw |
E01017290 | West Wight | 0.0 | 0.0 |
E01017291 | Central Wight | 0.0 | 0.0 |
E01017297 | Central Wight | 0.0 | 0.0 |
E01017298 | Chale, Niton and Whitwell | 0.0 | 0.0 |
E01017299 | Chale, Niton and Whitwell | 0.0 | 0.0 |
E01022489 | Burghclere, Highclere and St Mary Bourne | 0.0 | 0.0 |
Figure ?? repeats the analysis for electricity.
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.14: £k per LSOA incurred via electricity using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.15: £k per LSOA incurred via electricity using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 142.8 228.6 248.1 262.8 273.4 556.7
LAD11NM | WD18NM | LSOA11CD | elecTCO2e_pdw | beis_GBPtotal_c_elec_perdw |
Hart | Hartley Wintney | E01022858 | 2.3 | 556.7 |
Basingstoke and Deane | Burghclere, Highclere and St Mary Bourne | E01022489 | 2.2 | 531.1 |
New Forest | Forest North West | E01023026 | 2.1 | 523.8 |
Basingstoke and Deane | Burghclere, Highclere and St Mary Bourne | E01022514 | 2.1 | 519.9 |
Basingstoke and Deane | East Woodhay | E01022500 | 2.1 | 517.8 |
New Forest | Bramshaw, Copythorne North and Minstead | E01022993 | 2.1 | 512.3 |
LAD11NM | WD18NM | LSOA11CD | elecTCO2e_pdw | beis_GBPtotal_c_elec_perdw |
East Hampshire | Whitehill Pinewood | E01022639 | 0.6 | 142.8 |
Southampton | Woolston | E01017278 | 0.7 | 160.6 |
Portsmouth | Charles Dickens | E01017031 | 0.7 | 172.8 |
Portsmouth | St Thomas | E01017132 | 0.7 | 174.0 |
Portsmouth | Hilsea | E01017091 | 0.7 | 177.2 |
Portsmouth | Charles Dickens | E01017035 | 0.7 | 179.1 |
Figure ?? shows the same analysis for metered energy (elec + gas)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.16: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.17: £k per LSOA incurred via electricity and gas using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 275.3 671.0 775.8 789.6 903.9 1569.1
Figure ?? shows the same analysis for all home energy (elec + gas + oil etc)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.18: £k per LSOA incurred via all home heat energy using BEIS central carbon price
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.19: £k per LSOA incurred via all home heat energy using BEIS central carbon price
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 275.3 671.0 775.8 789.6 903.9 1569.1
Applied at to per dwelling values (not LSOA total)
Cut at 25%, 50% - so any emissions over 50% get high carbon cost
## Cuts for total per dw
## 0% 25% 50% 75% 100%
## 5802.128 15089.898 20566.083 27272.399 52773.034
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw
## 1: 19.50899 1840.968 1082.6772
## 2: 19.82781 1840.968 1160.7888
## 3: 13.99627 1707.545 0.0000
## 4: 20.65559 1840.968 1341.6653
## 5: 24.81776 1840.968 1341.6653
## 6: 23.21860 1840.968 1341.6653
## 7: 16.50536 1840.968 346.7874
## 8: 12.62994 1540.852 0.0000
## 9: 18.11356 1840.968 740.7964
## 10: 17.63948 1840.968 624.6469
## beis_GBPtotal_sc2_h_perdw beis_GBPtotal_sc2_perdw
## 1: 0.00000 2923.645
## 2: 0.00000 3001.756
## 3: 0.00000 1707.545
## 4: 32.84875 3215.482
## 5: 1560.36436 4742.997
## 6: 973.47545 4156.108
## 7: 0.00000 2187.755
## 8: 0.00000 1540.852
## 9: 0.00000 2581.764
## 10: 0.00000 2465.615
| Name | …[] |
| Number of rows | 1194 |
| Number of columns | 3 |
| Key | NULL |
| _______________________ | |
| Column type frequency: | |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| V1 | 0 | 1 | 21.83 | 8.87 | 5.80 | 15.09 | 20.57 | 27.27 | 52.77 | ▅▇▅▂▁ |
| beis_GBPtotal_sc2_perdw | 0 | 1 | 4107.16 | 2776.24 | 707.86 | 1842.36 | 3183.22 | 5643.85 | 15002.58 | ▇▃▂▁▁ |
| beis_GBPtotal_sc2 | 0 | 1 | 2764975.74 | 1564529.45 | 587844.80 | 1514526.30 | 2355174.80 | 3775189.52 | 7035326.91 | ▇▆▅▂▂ |
## nLSOAs sum_total_sc1 sum_total_sc2
## 1: 1194 4441.497 3301.381
## CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1: 2001.180 217.6592
## 2: 2192.751 217.6592
## 3: 2034.557 217.6592
## 4: 2154.426 217.6592
## 5: 2240.187 217.6592
## 6: 2316.899 217.6592
## CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw
## 1: 2001.180 217.6592 53.18664
## 2: 2192.751 217.6592 95.92818
## 3: 2034.557 217.6592 61.36401
## 4: 2154.426 217.6592 90.73196
## 5: 2240.187 217.6592 95.92818
## 6: 2316.899 217.6592 95.92818
## beis_GBPgas_sc2_h_perdw beis_GBPgas_sc2_perdw
## 1: 0.000000 270.8458
## 2: 6.281471 319.8688
## 3: 0.000000 279.0232
## 4: 0.000000 308.3911
## 5: 23.690540 337.2779
## 6: 51.843957 365.4313
## [1] 301.6514
## [1] 153.2484
## nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP
## 1: 1194 3301.381 301.6514 153.2484
Excludes EPC A, B & C (assumes no need to upgrade)
## To retrofit D-E (£m)
## [1] 6259.381
## Number of dwellings: 470630
## To retrofit F-G (£m)
## [1] 961.2963
## Number of dwellings: 35869
## To retrofit D-G (£m)
## [1] 7220.677
## To retrofit D-G (mean per dwelling £k)
## [1] 14.14921
meanRetrofitPerLSOA_m | totalRetrofit_m |
6.0 | 7,220.7 |
Plot retrofit costs
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.20 maps retrofit costs
Figure 5.20: LSOA retrofit costs (upgrade EPC D to G)
LAD11NM | WD18NM | LSOA11CD | epc_pc_A_C | retrofitSum |
Basingstoke and Deane | Rooksdown | E01032840 | 99.5 | 56,964.2 |
Hart | Fleet West | E01032854 | 97.0 | 250,281.8 |
Basingstoke and Deane | Popley West | E01032843 | 97.5 | 269,283.0 |
Portsmouth | Charles Dickens | E01033381 | 93.4 | 825,292.6 |
Basingstoke and Deane | Hatch Warren and Beggarwood | E01032849 | 88.7 | 854,505.8 |
Basingstoke and Deane | Brookvale and Kings Furlong | E01032846 | 92.3 | 901,769.1 |
LAD11NM | WD18NM | LSOA11CD | epc_pc_A_C | retrofitSum |
New Forest | Brockenhurst and Forest South East | E01022999 | 8.2 | 19,333,683.6 |
East Hampshire | Froxfield and Steep | E01022597 | 12.2 | 16,383,839.1 |
Isle of Wight | Cowes North | E01017300 | 40.3 | 15,404,574.4 |
Isle of Wight | Freshwater South | E01033239 | 26.1 | 14,972,795.5 |
East Hampshire | East Meon | E01022592 | 16.4 | 14,926,403.2 |
Isle of Wight | Nettlestone and Seaview | E01017351 | 21.0 | 14,534,696.0 |
Table 5.13 shows the overall results for Scenario 1.
LAD11NM | ons_hh_2020 | Total_Retrofit_Cost_m | Total_dwellings | Total_dwellings_retrofited | Total_All_emissions_Levy_m | Total_Gas_emissions_Levy_m | Total_Elec_emissions_Levy_m | Total_Metered_Energy_emissions_Levy_m | Total_Energy_emissions_Levy_m | HouseholdsRetrofitted_pc | Total_All_emissions_Levy_pc | Total_Metered_emissions_Levy_pc | Total_Energy_emissions_Levy_pc | Years_to_pay_all_e |
Basingstoke and Deane | 73,924.0 | 563.1 | 77,059 | 39,871.9 | 481.1 | 39.9 | 21.2 | 61.1 | 61.1 | 51.7 | 85.4 | 10.8 | 10.8 | 1.2 |
East Hampshire | 50,676.0 | 441.7 | 53,981 | 30,414.4 | 321.6 | 31.9 | 16.3 | 48.2 | 48.2 | 56.3 | 72.8 | 10.9 | 10.9 | 1.4 |
Eastleigh | 56,431.0 | 408.6 | 56,751 | 29,750.8 | 310.1 | 32.1 | 13.9 | 46.0 | 46.0 | 52.4 | 75.9 | 11.3 | 11.3 | 1.3 |
Fareham | 49,835.0 | 416.9 | 50,217 | 30,005.0 | 294.2 | 28.7 | 12.7 | 41.4 | 41.4 | 59.8 | 70.6 | 9.9 | 9.9 | 1.4 |
Gosport | 37,398.0 | 297.0 | 37,565 | 21,188.2 | 167.9 | 16.9 | 9.1 | 26.0 | 26.0 | 56.4 | 56.6 | 8.8 | 8.8 | 1.8 |
Hart | 38,300.0 | 315.6 | 40,188 | 22,514.6 | 292.6 | 28.8 | 11.7 | 40.5 | 40.5 | 56.0 | 92.7 | 12.8 | 12.8 | 1.1 |
Havant | 55,050.0 | 461.1 | 55,902 | 32,886.5 | 245.5 | 30.8 | 13.9 | 44.8 | 44.8 | 58.8 | 53.2 | 9.7 | 9.7 | 1.9 |
Isle of Wight | 65,591.0 | 685.2 | 72,393 | 45,809.7 | 231.8 | 32.3 | 18.3 | 50.6 | 50.6 | 63.3 | 33.8 | 7.4 | 7.4 | 3.0 |
New Forest | 79,893.0 | 745.3 | 82,065 | 52,106.0 | 408.8 | 46.1 | 22.5 | 68.6 | 68.6 | 63.5 | 54.9 | 9.2 | 9.2 | 1.8 |
Portsmouth | 90,188.0 | 802.0 | 90,855 | 57,205.5 | 370.8 | 42.4 | 20.8 | 63.1 | 63.1 | 63.0 | 46.2 | 7.9 | 7.9 | 2.2 |
Rushmoor | 37,619.0 | 309.3 | 39,700 | 22,310.5 | 246.0 | 23.1 | 9.7 | 32.8 | 32.8 | 56.2 | 79.5 | 10.6 | 10.6 | 1.3 |
Southampton | 101,661.0 | 908.1 | 108,605 | 62,731.8 | 435.3 | 44.8 | 26.7 | 71.4 | 71.4 | 57.8 | 47.9 | 7.9 | 7.9 | 2.1 |
Test Valley | 52,432.0 | 441.2 | 55,679 | 30,534.4 | 317.9 | 24.9 | 16.4 | 41.2 | 41.2 | 54.8 | 72.1 | 9.3 | 9.3 | 1.4 |
Winchester | 50,493.0 | 425.4 | 52,836 | 29,170.0 | 317.8 | 28.7 | 15.7 | 44.4 | 44.4 | 55.2 | 74.7 | 10.4 | 10.4 | 1.3 |
LAD11NM | Retrofit_Cost_pdw | All_emissions_Levy_pdw | Gas_emissions_Levy_pdw | Elec_emissions_Levy_pdw | meteredEnergy_emissions_Levy_pdw | Energy_emissions_Levy_pdw | All_emissions_Levy_pdw_pc | meteredEnergy_emissions_Levy_pdw_pc | Energy_emissions_Levy_pdw_pc |
Basingstoke and Deane | 13,977.4 | 6,510.7 | 524.7 | 278.4 | 803.0 | 803.0 | 46.6 | 5.7 | 5.7 |
East Hampshire | 14,334.8 | 6,249.2 | 605.0 | 298.8 | 903.8 | 903.8 | 43.6 | 6.3 | 6.3 |
Eastleigh | 13,733.3 | 5,702.3 | 573.8 | 247.0 | 820.8 | 820.8 | 41.5 | 6.0 | 6.0 |
Fareham | 13,884.2 | 6,054.9 | 572.9 | 253.6 | 826.5 | 826.5 | 43.6 | 6.0 | 6.0 |
Gosport | 13,957.9 | 4,689.6 | 464.9 | 241.4 | 706.3 | 706.3 | 33.6 | 5.1 | 5.1 |
Hart | 14,000.3 | 7,749.3 | 722.7 | 296.3 | 1,019.0 | 1,019.0 | 55.4 | 7.3 | 7.3 |
Havant | 13,959.7 | 4,505.1 | 551.7 | 250.0 | 801.8 | 801.8 | 32.3 | 5.7 | 5.7 |
Isle of Wight | 14,832.6 | 3,334.4 | 445.0 | 255.4 | 700.4 | 700.4 | 22.5 | 4.7 | 4.7 |
New Forest | 14,192.6 | 5,238.0 | 563.9 | 276.8 | 840.7 | 840.7 | 36.9 | 5.9 | 5.9 |
Portsmouth | 13,983.0 | 4,234.9 | 478.6 | 229.0 | 707.7 | 707.7 | 30.3 | 5.1 | 5.1 |
Rushmoor | 13,851.1 | 6,600.3 | 595.3 | 245.8 | 841.1 | 841.1 | 47.7 | 6.1 | 6.1 |
Southampton | 14,417.7 | 4,196.0 | 430.7 | 246.1 | 676.8 | 676.8 | 29.1 | 4.7 | 4.7 |
Test Valley | 14,233.6 | 6,210.3 | 463.6 | 295.8 | 759.4 | 759.4 | 43.6 | 5.3 | 5.3 |
Winchester | 14,388.0 | 6,239.7 | 549.6 | 294.4 | 844.0 | 844.0 | 43.4 | 5.9 | 5.9 |
## # Totals
## Total retrofit cost (£ m):
## [1] "7,221"
## Total dwellings retrofited:
## [1] "506,499"
## Out of (n elec meters):
## [1] "873,796"
## Out of (ONS n hh 2020):
## [1] "839,491"
## Total Year 1 levy (all):
## [1] "4,441"
## Total Year 1 levy (gas):
## [1] "451"
## Total Year 1 levy (elec):
## [1] "229"
If we exclude Southampton, Portsmouth & IoW the totals are…
##
## Basingstoke and Deane East Hampshire Eastleigh
## 1 1 1
## Fareham Gosport Hart
## 1 1 1
## Havant New Forest Rushmoor
## 1 1 1
## Test Valley Winchester
## 1 1
## # Totals
## Total retrofit cost (£ m):
## [1] "4,825"
## Total dwellings retrofited:
## [1] "340,752"
## Out of (n elec meters 2018):
## [1] "601,943"
## Out of (ons hh 2020):
## [1] "582,051"
## Total Year 1 levy (all):
## [1] "3,404"
## Total Year 1 levy (gas):
## [1] "332"
## Total Year 1 levy (elec):
## [1] "163"
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Repeat per dwelling
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Table 5.14 shows the overall results for Scenario 1.
LAD11NM | Total_Retrofit_Cost_m | Total_All_emissions_Levy_m_sc2 | Total_Gas_emissions_Levy_m_sc2 | Total_Elec_emissions_Levy_m_sc2 | Total_All_emissions_Levy_pc |
Basingstoke and Deane | 563.1 | 399.3 | 27.2 | 15.1 | 70.9 |
East Hampshire | 441.7 | 254.6 | 23.9 | 12.7 | 57.6 |
Eastleigh | 408.6 | 232.9 | 21.8 | 8.5 | 57.0 |
Fareham | 416.9 | 233.2 | 19.5 | 8.1 | 55.9 |
Gosport | 297.0 | 111.5 | 10.1 | 5.5 | 37.5 |
Hart | 315.6 | 267.6 | 23.9 | 8.8 | 84.8 |
Havant | 461.1 | 160.1 | 20.7 | 8.7 | 34.7 |
Isle of Wight | 685.2 | 125.6 | 18.7 | 11.7 | 18.3 |
New Forest | 745.3 | 292.5 | 32.2 | 15.7 | 39.2 |
Portsmouth | 802.0 | 233.4 | 24.9 | 11.9 | 29.1 |
Rushmoor | 309.3 | 204.6 | 15.9 | 6.0 | 66.2 |
Southampton | 908.1 | 274.4 | 25.9 | 16.3 | 30.2 |
Test Valley | 441.2 | 254.3 | 16.3 | 12.4 | 57.6 |
Winchester | 425.4 | 257.5 | 20.7 | 12.0 | 60.5 |
LAD11NM | Retrofit_Cost_pdw | All_emissions_Levy_pdw | Gas_emissions_Levy_pdw | Elec_emissions_Levy_pdw | All_emissions_Levy_pdw_pc |
Basingstoke and Deane | 13,977.4 | 5,551.1 | 361.8 | 200.5 | 39.7 |
East Hampshire | 14,334.8 | 5,136.3 | 455.6 | 230.2 | 35.8 |
Eastleigh | 13,733.3 | 4,415.6 | 392.9 | 152.1 | 32.2 |
Fareham | 13,884.2 | 4,914.7 | 390.3 | 161.6 | 35.4 |
Gosport | 13,957.9 | 3,208.7 | 280.9 | 145.2 | 23.0 |
Hart | 14,000.3 | 7,314.3 | 606.6 | 224.9 | 52.2 |
Havant | 13,959.7 | 2,978.5 | 369.5 | 156.2 | 21.3 |
Isle of Wight | 14,832.6 | 1,837.6 | 257.6 | 165.2 | 12.4 |
New Forest | 14,192.6 | 3,872.6 | 394.1 | 196.3 | 27.3 |
Portsmouth | 13,983.0 | 2,729.3 | 284.9 | 130.7 | 19.5 |
Rushmoor | 13,851.1 | 5,704.4 | 418.8 | 151.8 | 41.2 |
Southampton | 14,417.7 | 2,729.3 | 254.3 | 150.9 | 18.9 |
Test Valley | 14,233.6 | 5,156.4 | 313.4 | 224.6 | 36.2 |
Winchester | 14,388.0 | 5,198.8 | 398.9 | 223.3 | 36.1 |
## # Totals
## Total Year 1 levy (all):
## [1] "3,301"
## Total Year 1 levy (gas):
## [1] "302"
## Total Year 1 levy (elec):
## [1] "153"
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Repeat per dwelling
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.062 2.097 2.789 3.162 3.867 10.307
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.21: All levy payback years (Scenario 1)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.215 15.360 17.941 19.020 20.921 57.639
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.22: Payback if equal share (Scenario 1)
Figure 5.23: Payback if equal share (Scenario 1)
What happens in Year 2 totally depends on the rate of upgrades…
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.924 2.502 4.376 5.341 7.663 20.699
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Figure 5.24: All levy payback years (Scenario 2)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.215 15.360 17.941 19.020 20.921 57.639
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Figure 5.25: Payback if equal share (Scenario 2)
Figure 5.26: Payback if equal share (Scenario 2)
What happens in Year 2 totally depends on the rate of upgrades…
LAD11NM | Total_dwellings | Total_dwellings_retrofited | Total_Retrofit_Cost_m | Total_All_emissions_Levy_m | Total_Metered_Energy_emissions_Levy_m | YearsToPayViaMeteredEnergyEmissions |
Basingstoke and Deane | 77,059 | 39,871.9 | 563.1 | 481.1 | 61.1 | 9.2 |
East Hampshire | 53,981 | 30,414.4 | 441.7 | 321.6 | 48.2 | 9.2 |
Eastleigh | 56,751 | 29,750.8 | 408.6 | 310.1 | 46.0 | 8.9 |
Fareham | 50,217 | 30,005.0 | 416.9 | 294.2 | 41.4 | 10.1 |
Gosport | 37,565 | 21,188.2 | 297.0 | 167.9 | 26.0 | 11.4 |
Hart | 40,188 | 22,514.6 | 315.6 | 292.6 | 40.5 | 7.8 |
Havant | 55,902 | 32,886.5 | 461.1 | 245.5 | 44.8 | 10.3 |
Isle of Wight | 72,393 | 45,809.7 | 685.2 | 231.8 | 50.6 | 13.5 |
New Forest | 82,065 | 52,106.0 | 745.3 | 408.8 | 68.6 | 10.9 |
Portsmouth | 90,855 | 57,205.5 | 802.0 | 370.8 | 63.1 | 12.7 |
Rushmoor | 39,700 | 22,310.5 | 309.3 | 246.0 | 32.8 | 9.4 |
Southampton | 108,605 | 62,731.8 | 908.1 | 435.3 | 71.4 | 12.7 |
Test Valley | 55,679 | 30,534.4 | 441.2 | 317.9 | 41.2 | 10.7 |
Winchester | 52,836 | 29,170.0 | 425.4 | 317.8 | 44.4 | 9.6 |
Total_dwellings | Total_dwellings_retrofited | Total_Retrofit_Cost_m | Total_All_emissions_Levy_m | Total_Metered_Energy_emissions_Levy_m |
873,796 | 506,499.4 | 7,220.7 | 4,441.5 | 680.2 |
Figure 5.27 shows the year 1 levy under each scenario against total estimated retrofit costs.
Figure 5.27: Compare districts
Figure 5.28
Figure 5.28: Pay back scatter for all LSOAs
I don’t know if this will work…
## Doesn't